Data Scientists • Focused on replication, scaling, and supporting ML solutions across clients. • Responsibilities: • Deploy and operationalize models created by economists. • Onboard new partners (80+ airline clients). • Expand and refine existing solutions (e.g., reward improvements). • Requirements: • 4+ years of experience, degree in related field. • Ability to become self-sufficient over time and eventually take over economist responsibilities. Technical Stack & Tools • Primary ML algorithms: • Contextual Bandits (reinforcement learning) • XGBoost (baseline predictive models) • K-means clustering • Linear regression • Platforms: • Amazon SageMaker (Studio) • Redshift • Snowflake • Q for Business • Languages: • SQL • Python Use Cases & Deployment Strategy • Primary use cases: • Reinforcement learning for upgrade bidding. • Example: Customer receives an upgrade offer → suggest an alternative product/ancillary offer. • Hospitality use case: Ancillary services offered at random — models can optimize targeting. • New partner onboarding: • Begin with existing data (80 airline partners). • Economist monitors and customizes model. • Roll out partner by partner using shared framework. Amazon SageMaker Studio for ML development and orchestration. Algorithms like: • Contextual Bandits – used for real-time decision making (e.g., pricing, recommendations). • XGBoost – a high-performance gradient boosting algorithm, often used in tabular data for predictions. • K-means Clustering – for unsupervised segmentation or grouping tasks. • Linear Regression – for basic predictive modeling or as a baseline.